VacationPy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dependencies.\n",
    "import pandas as pd\n",
    "import os\n",
    "import gmaps\n",
    "import requests\n",
    "# Import the API key.\n",
    "from config import g_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City_ID</th>\n",
       "      <th>City</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Cidreira</td>\n",
       "      <td>-30.18</td>\n",
       "      <td>-50.21</td>\n",
       "      <td>68.88</td>\n",
       "      <td>93</td>\n",
       "      <td>97</td>\n",
       "      <td>7.36</td>\n",
       "      <td>BR</td>\n",
       "      <td>2020-10-21 01:50:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Lata</td>\n",
       "      <td>40.16</td>\n",
       "      <td>-8.33</td>\n",
       "      <td>61.00</td>\n",
       "      <td>94</td>\n",
       "      <td>17</td>\n",
       "      <td>1.01</td>\n",
       "      <td>PT</td>\n",
       "      <td>2020-10-21 01:50:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Port Alfred</td>\n",
       "      <td>-33.59</td>\n",
       "      <td>26.89</td>\n",
       "      <td>55.51</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>7.81</td>\n",
       "      <td>ZA</td>\n",
       "      <td>2020-10-21 01:50:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Yulara</td>\n",
       "      <td>-25.24</td>\n",
       "      <td>130.99</td>\n",
       "      <td>89.60</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>5.82</td>\n",
       "      <td>AU</td>\n",
       "      <td>2020-10-21 01:50:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Macusani</td>\n",
       "      <td>-14.08</td>\n",
       "      <td>-70.43</td>\n",
       "      <td>38.32</td>\n",
       "      <td>77</td>\n",
       "      <td>94</td>\n",
       "      <td>5.99</td>\n",
       "      <td>PE</td>\n",
       "      <td>2020-10-21 01:50:45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   City_ID         City    Lat     Lng  Max Temp  Humidity  Cloudiness  \\\n",
       "0        0     Cidreira -30.18  -50.21     68.88        93          97   \n",
       "1        1         Lata  40.16   -8.33     61.00        94          17   \n",
       "2        2  Port Alfred -33.59   26.89     55.51        76           0   \n",
       "3        3       Yulara -25.24  130.99     89.60        31           0   \n",
       "4        4     Macusani -14.08  -70.43     38.32        77          94   \n",
       "\n",
       "   Wind Speed Country                 Date  \n",
       "0        7.36      BR  2020-10-21 01:50:44  \n",
       "1        1.01      PT  2020-10-21 01:50:44  \n",
       "2        7.81      ZA  2020-10-21 01:50:44  \n",
       "3        5.82      AU  2020-10-21 01:50:45  \n",
       "4        5.99      PE  2020-10-21 01:50:45  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Store the CSV you saved created in part one into a dataframe.\n",
    "city_data_df = pd.read_csv(\"weather_data/cities.csv\")\n",
    "city_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_ID         int64\n",
       "City           object\n",
       "Lat           float64\n",
       "Lng           float64\n",
       "Max Temp      float64\n",
       "Humidity        int64\n",
       "Cloudiness      int64\n",
       "Wind Speed    float64\n",
       "Country        object\n",
       "Date           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Configure gmaps to use my Google API key.\n",
    "import gmaps\n",
    "gmaps.configure(api_key = g_key)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the maximum temperature.\n",
    "max_temp = city_data_df[\"Max Temp\"]\n",
    "temps = []\n",
    "for temp in max_temp:\n",
    "    temps.append(max(temp, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "10fb7b64027944c1b547ff860a56db8b",
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      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap of temperature\n",
    "# Get the latitude and longitude.\n",
    "locations = city_data_df[[\"Lat\", \"Lng\"]]\n",
    "# Get the maximum temperature.\n",
    "max_temp = city_data_df[\"Max Temp\"]\n",
    "# Assign the figure variable.\n",
    "fig = gmaps.figure()\n",
    "# Assign the heatmap variable.\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=temps, dissipating=False, max_intensity=300, point_radius=4)\n",
    "# Add the heatmap layer.\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap of percent humidity\n",
    "locations = city_data_df[[\"Lat\", \"Lng\"]]\n",
    "humidity = city_data_df[\"Humidity\"]\n",
    "fig = gmaps.figure(center=(30.0, 31.0), zoom_level=1.5)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=humidity, dissipating=False, max_intensity=300, point_radius=4)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap of percent cloudiness\n",
    "locations = city_data_df[[\"Lat\", \"Lng\"]]\n",
    "clouds = city_data_df[\"Cloudiness\"]\n",
    "fig = gmaps.figure(center=(30.0, 31.0), zoom_level=1.5)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=clouds, dissipating=False, max_intensity=300, point_radius=4)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "d7d14cd598d14ac6989d7c7fa4b6515b",
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      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap of percent cloudiness\n",
    "locations = city_data_df[[\"Lat\", \"Lng\"]]\n",
    "wind = city_data_df[\"Wind Speed\"]\n",
    "fig = gmaps.figure(center=(30.0, 31.0), zoom_level=1.5)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=wind, dissipating=False, max_intensity=300, point_radius=4)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the minimum temperature you would like for your trip? 75\n",
      "What is the maximum temperature you would like for your trip? 90\n"
     ]
    }
   ],
   "source": [
    "# Ask the customer to add a minimum and maximum temperature value.\n",
    "min_temp = float(input(\"What is the minimum temperature you would like for your trip? \"))\n",
    "max_temp = float(input(\"What is the maximum temperature you would like for your trip? \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City_ID</th>\n",
       "      <th>City</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Yulara</td>\n",
       "      <td>-25.24</td>\n",
       "      <td>130.99</td>\n",
       "      <td>89.60</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>5.82</td>\n",
       "      <td>AU</td>\n",
       "      <td>2020-10-21 01:50:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>29.42</td>\n",
       "      <td>-98.49</td>\n",
       "      <td>84.20</td>\n",
       "      <td>54</td>\n",
       "      <td>40</td>\n",
       "      <td>20.80</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-10-21 01:49:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Moindou</td>\n",
       "      <td>-21.69</td>\n",
       "      <td>165.68</td>\n",
       "      <td>82.40</td>\n",
       "      <td>54</td>\n",
       "      <td>75</td>\n",
       "      <td>13.87</td>\n",
       "      <td>NC</td>\n",
       "      <td>2020-10-21 01:50:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Sarangani</td>\n",
       "      <td>5.40</td>\n",
       "      <td>125.46</td>\n",
       "      <td>84.07</td>\n",
       "      <td>74</td>\n",
       "      <td>92</td>\n",
       "      <td>9.82</td>\n",
       "      <td>PH</td>\n",
       "      <td>2020-10-21 01:50:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Illela</td>\n",
       "      <td>13.73</td>\n",
       "      <td>5.30</td>\n",
       "      <td>82.15</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1.16</td>\n",
       "      <td>NG</td>\n",
       "      <td>2020-10-21 01:50:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Alta Floresta</td>\n",
       "      <td>-9.88</td>\n",
       "      <td>-56.09</td>\n",
       "      <td>77.74</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.36</td>\n",
       "      <td>BR</td>\n",
       "      <td>2020-10-21 01:47:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>Butaritari</td>\n",
       "      <td>3.07</td>\n",
       "      <td>172.79</td>\n",
       "      <td>82.38</td>\n",
       "      <td>73</td>\n",
       "      <td>46</td>\n",
       "      <td>10.36</td>\n",
       "      <td>KI</td>\n",
       "      <td>2020-10-21 01:50:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>Faanui</td>\n",
       "      <td>-16.48</td>\n",
       "      <td>-151.75</td>\n",
       "      <td>78.96</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>14.27</td>\n",
       "      <td>PF</td>\n",
       "      <td>2020-10-21 01:50:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>Kapaa</td>\n",
       "      <td>22.08</td>\n",
       "      <td>-159.32</td>\n",
       "      <td>81.00</td>\n",
       "      <td>78</td>\n",
       "      <td>90</td>\n",
       "      <td>4.70</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-10-21 01:50:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>Mayumba</td>\n",
       "      <td>-3.43</td>\n",
       "      <td>10.66</td>\n",
       "      <td>76.69</td>\n",
       "      <td>88</td>\n",
       "      <td>100</td>\n",
       "      <td>6.08</td>\n",
       "      <td>GA</td>\n",
       "      <td>2020-10-21 01:50:52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    City_ID           City    Lat     Lng  Max Temp  Humidity  Cloudiness  \\\n",
       "3         3         Yulara -25.24  130.99     89.60        31           0   \n",
       "11       11    San Antonio  29.42  -98.49     84.20        54          40   \n",
       "12       12        Moindou -21.69  165.68     82.40        54          75   \n",
       "15       15      Sarangani   5.40  125.46     84.07        74          92   \n",
       "17       17         Illela  13.73    5.30     82.15        27           0   \n",
       "20       20  Alta Floresta  -9.88  -56.09     77.74        62           0   \n",
       "32       32     Butaritari   3.07  172.79     82.38        73          46   \n",
       "33       33         Faanui -16.48 -151.75     78.96        80           1   \n",
       "34       34          Kapaa  22.08 -159.32     81.00        78          90   \n",
       "39       39        Mayumba  -3.43   10.66     76.69        88         100   \n",
       "\n",
       "    Wind Speed Country                 Date  \n",
       "3         5.82      AU  2020-10-21 01:50:45  \n",
       "11       20.80      US  2020-10-21 01:49:23  \n",
       "12       13.87      NC  2020-10-21 01:50:46  \n",
       "15        9.82      PH  2020-10-21 01:50:47  \n",
       "17        1.16      NG  2020-10-21 01:50:47  \n",
       "20        0.36      BR  2020-10-21 01:47:55  \n",
       "32       10.36      KI  2020-10-21 01:50:51  \n",
       "33       14.27      PF  2020-10-21 01:50:51  \n",
       "34        4.70      US  2020-10-21 01:50:51  \n",
       "39        6.08      GA  2020-10-21 01:50:52  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the dataset to find the cities that fit the criteria.\n",
    "preferred_cities_df = city_data_df.loc[(city_data_df[\"Max Temp\"] <= max_temp) & \\\n",
    "                                       (city_data_df[\"Max Temp\"] >= min_temp)]\n",
    "preferred_cities_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_ID       171\n",
       "City          171\n",
       "Lat           171\n",
       "Lng           171\n",
       "Max Temp      171\n",
       "Humidity      171\n",
       "Cloudiness    171\n",
       "Wind Speed    171\n",
       "Country       171\n",
       "Date          171\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preferred_cities_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Hotel Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yulara</td>\n",
       "      <td>AU</td>\n",
       "      <td>89.60</td>\n",
       "      <td>-25.24</td>\n",
       "      <td>130.99</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>San Antonio</td>\n",
       "      <td>US</td>\n",
       "      <td>84.20</td>\n",
       "      <td>29.42</td>\n",
       "      <td>-98.49</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Moindou</td>\n",
       "      <td>NC</td>\n",
       "      <td>82.40</td>\n",
       "      <td>-21.69</td>\n",
       "      <td>165.68</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sarangani</td>\n",
       "      <td>PH</td>\n",
       "      <td>84.07</td>\n",
       "      <td>5.40</td>\n",
       "      <td>125.46</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Illela</td>\n",
       "      <td>NG</td>\n",
       "      <td>82.15</td>\n",
       "      <td>13.73</td>\n",
       "      <td>5.30</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Alta Floresta</td>\n",
       "      <td>BR</td>\n",
       "      <td>77.74</td>\n",
       "      <td>-9.88</td>\n",
       "      <td>-56.09</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Butaritari</td>\n",
       "      <td>KI</td>\n",
       "      <td>82.38</td>\n",
       "      <td>3.07</td>\n",
       "      <td>172.79</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Faanui</td>\n",
       "      <td>PF</td>\n",
       "      <td>78.96</td>\n",
       "      <td>-16.48</td>\n",
       "      <td>-151.75</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Kapaa</td>\n",
       "      <td>US</td>\n",
       "      <td>81.00</td>\n",
       "      <td>22.08</td>\n",
       "      <td>-159.32</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Mayumba</td>\n",
       "      <td>GA</td>\n",
       "      <td>76.69</td>\n",
       "      <td>-3.43</td>\n",
       "      <td>10.66</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             City Country  Max Temp    Lat     Lng Hotel Name\n",
       "3          Yulara      AU     89.60 -25.24  130.99           \n",
       "11    San Antonio      US     84.20  29.42  -98.49           \n",
       "12        Moindou      NC     82.40 -21.69  165.68           \n",
       "15      Sarangani      PH     84.07   5.40  125.46           \n",
       "17         Illela      NG     82.15  13.73    5.30           \n",
       "20  Alta Floresta      BR     77.74  -9.88  -56.09           \n",
       "32     Butaritari      KI     82.38   3.07  172.79           \n",
       "33         Faanui      PF     78.96 -16.48 -151.75           \n",
       "34          Kapaa      US     81.00  22.08 -159.32           \n",
       "39        Mayumba      GA     76.69  -3.43   10.66           "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create DataFrame called hotel_df to store hotel names along with city, country, max temp, and coordinates.\n",
    "hotel_df = preferred_cities_df[[\"City\", \"Country\", \"Max Temp\", \"Lat\", \"Lng\"]].copy()\n",
    "hotel_df[\"Hotel Name\"] = \"\"\n",
    "hotel_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set parameters to search for a hotel.\n",
    "params = {\"radius\": 5000,\"type\": \"lodging\",\"key\": g_key}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n"
     ]
    }
   ],
   "source": [
    "# Iterate through the DataFrame.\n",
    "for index, row in hotel_df.iterrows():\n",
    "    # Get the latitude and longitude.\n",
    "    lat = row[\"Lat\"]\n",
    "    lng = row[\"Lng\"]\n",
    "\n",
    "    # Add the latitude and longitude to location key for the params dictionary.\n",
    "    params[\"location\"] = f\"{lat},{lng}\"\n",
    "\n",
    "    # Use the search term: \"lodging\" and our latitude and longitude.\n",
    "    base_url = \"https://maps.googleapis.com/maps/api/place/nearbysearch/json\"\n",
    "    # Make request and get the JSON data from the search.\n",
    "    hotels = requests.get(base_url, params=params).json()\n",
    "    # Grab the first hotel from the results and store the name.\n",
    "    try:\n",
    "        hotel_df.loc[index, \"Hotel Name\"] = hotels[\"results\"][0][\"name\"]\n",
    "    except (IndexError):\n",
    "        print(\"Hotel not found... skipping.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the DataFrame Row.\n",
    "info_box_template = \"\"\"\n",
    "<dl>\n",
    "<dt>Hotel Name</dt><dd>{Hotel Name}</dd>\n",
    "<dt>City</dt><dd>{City}</dd>\n",
    "<dt>Country</dt><dd>{Country}</dd>\n",
    "<dt>Max Temp</dt><dd>{Max Temp} °F</dd>\n",
    "</dl>\n",
    "\"\"\"\n",
    "hotel_info = [info_box_template.format(**row) for index, row in hotel_df.iterrows()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a89811a0690f46a9b421e4e64203e273",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add a heatmap of temperature for the vacation spots.\n",
    "locations = hotel_df[[\"Lat\", \"Lng\"]]\n",
    "max_temp = hotel_df[\"Max Temp\"]\n",
    "fig = gmaps.figure(center=(30.0, 31.0), zoom_level=1.5)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=max_temp, dissipating=False,\n",
    "             max_intensity=300, point_radius=4)\n",
    "marker_layer = gmaps.marker_layer(locations, info_box_content=hotel_info)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "fig.add_layer(marker_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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